﻿[Script Info] Title: [Events] Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text Dialogue: 0,0:00:00.25,0:00:05.37,Default,,0000,0000,0000,,One common measure of affect size, when comparing means, is Cohen's d. Named Dialogue: 0,0:00:05.37,0:00:10.30,Default,,0000,0000,0000,,after the statistition Jacob Cohen. Cohen's d is a standardized mean difference Dialogue: 0,0:00:10.30,0:00:15.56,Default,,0000,0000,0000,,that measures the distance between 2 means in standard deviation units. In Dialogue: 0,0:00:15.56,0:00:19.56,Default,,0000,0000,0000,,other words, instead of dividing by standard error. We simply divide by the Dialogue: 0,0:00:19.56,0:00:23.92,Default,,0000,0000,0000,,standard deviation of the sample. We can think of it like this, we have our Dialogue: 0,0:00:23.92,0:00:29.55,Default,,0000,0000,0000,,sample, let's just say it's normally distributed. And here's the sample mean, Dialogue: 0,0:00:29.55,0:00:33.97,Default,,0000,0000,0000,,and the standard deviation of our sample is S. Now let's say we have some Dialogue: 0,0:00:33.97,0:00:41.63,Default,,0000,0000,0000,,population mean out here. How many S's fit between x-bar and the mean? The Dialogue: 0,0:00:41.63,0:00:44.78,Default,,0000,0000,0000,,larger Cohen's d is, the further x-bar is from mu-not, in terms of the sample Dialogue: 0,0:00:44.78,0:00:50.35,Default,,0000,0000,0000,,standard deviation. So, go ahead and calculate Cohen's d for this example.